The invention pertains generally to a processor for waveforms, such as those obtained from the human body by surface electrodes during cardiac depolarizations, i.e. ECG waveforms, and is more particularly directed to detecting particular features of those waveforms requiring analysis at high resolutions.
The electrocardiographic (ECG or EKG) waveform has been the object of intensive study for a long time. Generally, the amplitude and waveshape of the QRS complex of such waveforms are of interest in the diagnosis of cardiac insufficiency and disease. Analog devices for plotting the waveshapes of heartbeats have been used extensively and numerous apparatus have been provided for detecting the peak amplitude of the R-segment and the onset of the waveform. Others devices have compared the shapes of the T-segment against emperical criteria to determine factors concerning cardiac function. All of these devices however, require human interpretation of the results and are generally of low resolution.
More recently the ECG has been studied in an attempt to discriminate small amplitude high frequency potentials occurring in the waveforms. Three of these microvolt level signals generating considerable interest today are His bundle potentials, high frequency components of the P-segment and QRS-segment of the waveform, and post-QRS segment "late potentials". It is believed that other useful microvolt signal information will yet be discovered but has not been identified because of the lack of instrumentation available to measure it reliably.
His bundle potentials, named for their discoveror, and other special high frequency features in the ECG waveform are electrical displays of the subcycle electrical activity taking place in the depolarization cycle other than just the major atrial and ventricular contractions. By being able to detect these special features, researchers will be better able to map the entire cardiac cycle and possibly diagnose problems of latent dysfunctions that are hidden today in the ECG waveform.
Small amplitude, high frequency microvolt signals after the QRS complex which are termed "late potentials" are useful in predicting ventricular tachycardia in post myocardial infarction patients. See Uther et al. "The Detection of Delayed Activation Signals of Low Amplitude in the Vector Cardiogram of Patients with Recurrent Ventricular Tachycardia by Signal Averaging", Excerpta Medica, Amsterdam, 1978, pp. 80-82. It is hypothesized that the scarring caused by an infarction produces nonuniform borders which modify the natural depolarization process to produce these potentials possibly by slowing the propagation of the normal potential through the tissue or by increasing the path length over which it travels.
Although all of these microvolt level signals are now useful and it is believed researchers will begin isolating others, these signals are extremely difficult to detect with consistency. The initial discrimination problem is found in the form of the ECG waveform itself. The amplitude of the QRS segment in a ECG signal is relatively high and at a relatively low frequency compared to those of the features of interest, which have a relatively high frequency and relatively low amplitude. Because of the difference in amplitudes between that which is measured and that which is of interest, a high resolution detection system must be used. In the digital context increasing the resolution in a detection apparatus generally means increasing the cost for including devices and memories of higher bit capacities.
Further, because of the frequency difference between the QRS complex and the microvolt potentials, selective detectors with various special characteristics must be used. For example to detect "late potentials" for tachycardia prediction, a high pass filter without any ringing time constant is necessary. This is because the "late potentials" which follow the high energy QRS-segment of the waveform will be masked by any ringing caused by the high energy portion of the signal. Additionally, base line shift of the ECG signal is detrimental because the DC component of the ECG signal may float above and below an average to an extent greater than the microvolt amplitudes one is trying to detect.
The input beat waveforms of an ECG signal may also contain false or odd beats such as muscle artifacts, noise, and ectopic beats. High frequency low amplitude features of an ECG signal are hard to detect in these circumstances because each beat cycle of the depolarization may be different from any other beat cycle and different depending on the positioning the electrodes measuring the waveform. Therefore, to be of real use a large number of beat waveforms must be summed to produce a composite average waveform over a number of beats while using objective criteria to reject spurious data. Only when a representative composite beat waveform is free from noise, base line shift, and odd beats can the microvolt potentials of interest be detected reliably.
Even when a number of waveforms have been sampled and odd or inconsistent beats have been eliminated therefrom, the problem remains as how to average the beat waveforms which are sampled from non-aligned times in the sampling process. Although there are landmarks in each beat, they can vary from cycle to cycle and provide only a relative alignment. The alignment of the waveforms is further problematical because of the effects of noise and the voltage variations of the waveforms on a beat to beat basis. There are several sources of induced voltage variation in these waveforms, but respiration is probably the main one with a stable heart rate. If the beat waveforms are not aligned with accuracy, the error in alignment acts as a filter to which frequency components of the composite waveform. Therefore, in the detection of microvolt potentials a misalignment during the averaging process can reverse the gains produced by averaging and mask these signals altogether.